Defined data patterns for object handling

ABSTRACT

A method of defining data patterns for object handling includes obtaining an image of an input data area, processing the image to obtain image data, and comparing the image data with a pattern, wherein the pattern identifies spatial information of corresponding pattern fields of the pattern. The method further includes determining a confidence level of the comparison of the image data according to a success in matching the image data with the pattern fields, comparing the confidence level with a confidence threshold associated with the pattern, and selecting the pattern. A pattern output associated with the selected pattern is identified, wherein the pattern output corresponds to a canonical return format, and the pattern output is applied to the image data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/917,371, filed Nov. 1, 2010, which is hereby incorporated byreference in its entirety.

COPYRIGHT NOTICE

© 2013 RAF Technology, Inc. A portion of the disclosure of this patentdocument contains material which is subject to copyright protection. Thecopyright owner has no objection to the facsimile reproduction by anyoneof the patent document or the patent disclosure, as it appears in thePatent and Trademark Office patent file or records, but otherwisereserves all copyright rights whatsoever. 37 CFR §1.71(d).

TECHNICAL FIELD

This invention pertains to methods and apparatus for identifying,sorting, delivering, or classifying objects, such as mail pieces andparts, including addresses, identifications, and labels.

BACKGROUND OF THE INVENTION

On average 20% of the overall mail stream is unrecognizable due tomisspelling, abbreviated street & city names, and/or improperlyaddressed or structured. Current directories require a tradeoff inadaptability & record volume vs. performance by forcing the user toincorporate stringent data management policies to achieve high addressassignment rates.

SUMMARY OF THE INVENTION

The following is a summary of the invention in order to provide a basicunderstanding of some aspects of the invention. This summary is notintended to identify key/critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome concepts of the invention in a simplified form as a prelude to themore detailed description that is presented later.

In one embodiment, a system presents a set of images of mail pieces orother input with each one parsed into lines and regions and eachcomponent labeled with what the component is (e.g. STATE, ZIP, ADDRESSEENAME). After such parsing and labeling, the system then takes the parsedand labeled set of images and deduces the allowed patterns in theaddresses for that country (or incoming application). The systemprovides the ability to extract the patterns automatically from such alabeled set of images.

In another embodiment, a system utilizes an optical characterrecognition OCR Engine to assign matched and verified addresses to moreoutgoing or incoming mail by accurately adapting and correcting variousdegrees of address corruption. The system reduces the level of characternoise, extracts all relevant envelope data, and then uses fuzzy logic,sophisticated pattern matching algorithms, and flexible search rules tomaximize the number of assignments.

The system may be configured to identify or categorize informationassociated with part labels or mail addresses for purposes of routingthe object (e.g. mail), monitoring inventory or parts usage, identifyingor selecting a common canonical format compatible with a plurality ofdifferent labeling formats, identifying missing or ambiguousinformation, and associating related or corresponding information in adatabase.

In another embodiment, a system comprises an device configured to obtainan image of a mail piece being sorted, delivered, or classified. Animage recognition device is configured to process the image into imagedata, and a processor is configured to compare the image data with apattern, wherein the pattern identifies spatial information ofcorresponding pattern field elements. The processor may further beconfigured to determine a confidence level of the comparison of theimage data according to a success in matching the image data with thepattern fields and compare the confidence level with a confidencethreshold associated with the pattern. If the confidence threshold ismet, the pattern is selected. The processor identifies a pattern outputassociated with the selected pattern, wherein the pattern outputcorresponds to a standardized mail address format. The system mayfurther comprise an output device configured to apply the pattern outputto the mail piece according to the selected pattern.

Additional aspects and advantages of this invention will be apparentfrom the following detailed description of preferred embodiments, whichproceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an object handling or directory system configured toimage and identify, sort, deliver, and/or otherwise classify an object.

FIG. 2 illustrates a graphical example of an address block and a patternthat might match to it.

FIG. 3 illustrates a screen shot of a mail processing system's visualdisplay. This display shows an image of a mail piece which has undergoneoptical character recognition, with the result then sent to thedirectory system, returning recognized and matched and verified address.

FIG. 4 illustrates the sequence used when the directory system parses anobject, in this case a “ParseAddress”.

FIG. 5 illustrates the overall system including both the front-enddirectory data compiler and a back-end runtime pattern identificationand categorization data files.

FIG. 6 graphically represents an example pattern comprising an addressblock and field descriptors.

FIG. 7 illustrates a process of identifying, imagining, matching,verifying classifying and delivering the results of an object match.

FIG. 8 is an example screen shot of the mail processing system's visualdisplay. It illustrates the use of the directory system to returnstandardized output (86 and 88) based on matching and verifying anaddress.

FIG. 9 illustrates the mail processing system using the directorysystem's fuzzy logic to return verified and matched informationassociated with an image of an address.

FIG. 10 is an example screen shot of the mail processing system's visualdisplay. It illustrates the use of the directory system's aliasingcapabilities to associate and match toan alias with informationidentified on an image of an address.

FIG. 11 is an example screen shot of the mail processing system's visualdisplay. It illustrates the identification of geo-positional datarelated to the data matched on the image from a scanned address.

FIG. 12 illustrates an example process of automatically generatingpatterns from a number of sample objects.

FIG. 13 illustrates an automatic pattern generation system 270.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 illustrates an object handling or directory system 130 configuredto image and identify, sort, deliver, and/or otherwise classify anobject. Objects to be analyzed, identified, sorted, delivered, orclassified are fed into the system 130 at the object infeed 140 beforebeing processed and ultimately removed at the exit or as sortationcompletes 150. The object may be processed or operated on by any or allof a control 136, reader 152, camera 158, printer/sprayer 154, and/or alabeler 156.

A directory system 125 is illustrated as including a parser 121,patterns 122, address records 123, data files and tables 124, and one ormore logs 126. An image processing system 135 is illustrated asincluding a database 131, image capture block 132, and an OCR system133, that includes a block field line locator 134. An interface system145 is illustrated as including a visual display 142 and operatorconsole 144. A network 120 may operatively connect the directory system125, image processing system 135, and interface system 145. A sortationdevice may be used to physically move, deliver, or sort the objectsthrough the system 130.

The system 130 may be configured to present a set of images of mailpieces, each one to be parsed into regions of interest and then lines oftext with each identified component ultimately to be matched with whatthe component is (e.g. STATE, ZIP, ADDRESSEE NAME). The customer hasprovided an address block description or pattern 122, Address Records123 and other Data Files and Tables 124. The OCR system 133, uses theBlock-Field-Line Locator 134 to identify a region of interest or AddressBlock and subsequently the individual lines within that Address Blockdata. This line data is passed on to the Directory System 125, which isthen uses the customer supplied patterns 122, data files 124, addressrecords 122 and parser 121 to identify individual address components andaddresses in each image. The individual components found on the imagesare identified. This identification is not with what they say, but withwhat they are or what they were meant to be. For example, the labellabels the entity that says “Washington” with “STATE”.

Included in such parsing, the system 130 takes the parsed image data anddeduces the allowed patterns in the addresses for that area (orcategory). For example, it can be determined that the bottom-most line(the parser detects lines) has the rightward-most entity labeled“ZIP-5”, the one to the right of that labeled “STATE” and the remaining,leftward-most entity labeled “CITY”. It can therefore be deduced thatCITY->STATE->ZIP on the bottom-most line is an allowed pattern that maybe matched.

The Physical Object

FIG. 2 illustrates a graphical example of an address block 25 and apattern 20 that might match to it. In a mail system, the physical object10 is a mail piece. It may, however, be such things as a part name andpart description as found in the manufacture or maintenance of anairplane, a business card, or almost any object that containsinformation on it that, when properly interpreted, tells the system whatto do with the object based on the definition and use of the patternsdiscussed below.

A mail piece has on it one or more addresses. For discussion purposes,assume the intent of the system is to determine where that mail pieceshould be routed, using the destination address printed on the addressblock 25 of the mail piece. A typical mail piece might contain anaddressee, a street number and name, a city, a neighborhood, asub-neighborhood (barrio in Mexico, for example), and other information.The goal of the mail piece processing is to route the mail piece along aparticular set of intermediate destinations (e.g. sorting tables) untilit reaches the intended addressee. For details on how the mail piece maybe routed, see U.S. patent application Ser. No. 12/711,202, the contentsof which are herein incorporated by reference in their entirety.

As previously discussed, there are at least two kinds of informationthat may be extracted from the object 10, including the objectinformation and the categorizing information. This information may beother than positional. A simple example would be finding a ZIP code bythe fact that it matches the NNNNN or the NNNNN-NNNN patterns regardlessof where it appears on the mail piece. For non-mail piece objects thiscategorization information may be arbitrarily complex and, indeed, mayrequire several rounds of parsing, database consulting, and rejection todetermine even what this categorization information actually is.

In one example, the object information obtained from a mail piece maycontain one or more strings of characters (possibly with alternativesand confidences). The categorizing information may include or identifywhere those characters appear on the mail piece (e.g. spatial, location,or positional information).

Categorizing information may identify what line the object informationis on, and how far from a reference point the object information islocated (e.g. the left-hand edge of the address block), etc. Thisinformation may be used to enable the parser to determine what to dowith the object data (i.e. to determine to what category it belongs).This actual category of this data may not appear explicitly on theobject, but be deduced from, for example, positional information onobject data obtained by the parser.

The Defined Pattern Set

How and where various address components appears on the mail piece andwhat those components represent is combined with other elements of thedirectory system 130 (FIG. 1) to produce a novel object handling system.

How and where various address components appears on the mail piece andwhat those components represent may be combined with other elements ofthe pattern generation system 270 (as shown in FIG. 13) to produce afurther embodiment of a novel object handling system.

A defined pattern may exist in a previously existing form or it may becreated by a user for a particular application. As an example of theformer case, the Universal Postal Union (UPU) often provides guidance asto where on a mail piece the addressee name, the city name, etc. mayappear for various countries. Such definition is almost alwaysdescriptive rather than proscriptive and often very approximate. In thelatter case, a customer may establish a set of patterns for use in hisapplication. Incoming mail and a country just establishing itsaddressing standards would be examples of the latter, as would use ofthe system for handling parts on a Boeing aircraft.

A typical address defined pattern 20 (FIG. 2) might identify:

-   -   The top line 22 contains addressee name and may be located just        below a 4-state bar code near the middle of the envelope.    -   The bottom line 28 contains the city name and/or country and,        optionally, the government code for the address. The code may be        located to the left of the city name and follows the pattern        ANANNA, where A is and alphabetic and N a numeric character.    -   The (third) line 26 above the bottom line may contain the        province name and the district name.    -   There may be additional lines 24 below the Addressee line, for        example, which identifies a street name or house number.

The pattern 20 may associate two principle kinds of information: adesignator for information type (city name, addressee name, etc.) andcategorizing information that allows the parser to determine what kindof object information it has found on the object. An example ofcategorizing information might be where a ZIP code can be located on amail piece. Location can be either absolute (say from the leading edgeof an envelope) or relative (with reference, say, to the left-hand edgeof the address block or to another element on the envelope). Thefour-digit extension of the ZIP Code may be located to the right of thefive-digit ZIP Code in the US. In addition, the pattern 20 may containinformation on whether each of the pattern elements must appear or areoptional.

Because the system 130 can apply fuzzy matching throughout the parsingprocess (see below), the matches to names may be inexact or exact, theirplacement may be inexact or exact, and even a “required” item may bemissing and the object handled provided the remaining information allowsunique classification via one or more of the patterns.

Often a single defined pattern is not sufficient for a broad set ofobjects. There are two reasons for this. Consider the case of anaddress. First, there might be multiple allowed or commonly used addressforms, each of which has its own defined pattern. Second, the address onthe envelope may be sufficiently incomplete, inaccurate, or ambiguousthat it must be approached from several different perspectives beforeproper confidence in how to handle it can be achieved. Third,user-specific business rules may impose additional constraints or orderof precedence that is reflected in the patterns. For example, a user mayrequire that the result of a pattern for which city and provinceelements match exactly be preferred over the result of a pattern withonly a postcode match.

A pattern can provide a description of the expected address and adescription of the output that is to be returned with a match on aparticular pattern. The output data may be described using meta-tags,constant data, spatial relationships, and constant strings like“Recipient:”. Meta-tags in the patterns may be ordered so as to instructwhich tag to attempt to match on first. If no match value is found forthe higher priority tag values, the system moves on to the next pattern.Meta-tag values also may have qualifiers, like, ‘require exact match’ or‘allow abbreviation match’. The system can use the ordering of eachelement in the pattern to predetermine and pre-limit possible candidatevalues for subsequent data elements.

The output description is associated with a match on the particularpattern, including a result weighting. There may be from one to Npatterns used to match on a given set of inputs. These are checkedagainst the input data in an ordered manner until a match is made. Theresult is weighted (ie. for the confidence in the result) and qualifiedas a finalization or non-finalization. The output is pulled from thedata base row that this pattern matches to so may include any data inthat data base row.

There may be a defined order to the patterns to be applied to aparticular object, with the intention that some be executed first andothers later, perhaps conditionally. A pattern may be set early in theorder of processing because it allows a more complete classification ofthe data from the object, because it is more likely to provideunambiguous results, is more likely to occur, or for many other reasons.

Based on a given object 10 (FIG. 2) such as a mail piece, the directorysystem may have several predefined patterns that include different setsof categories or different spatial associations with one or more of thecategories. A first pattern may be selected that is a preferred formator template, or one that simply includes a more complete address orlarger number of categories. The directory system may analyze the imageof the object 10 by sequentially comparing the object information withthe category information of the selected pattern. If the first objectinformation (or first field) of the object 10 matches the first categoryinformation of the selected pattern, than the directory system mayproceed to a next object information (or second field) for comparisonwith the category information.

A successful matching of one or more of the object information mayassist the directory system in interpreting or identifying objectinformation in a subsequent field. For example, if a first objectinformation identifies a state of an address, the directory system mayutilize the state information to determine which city is included in theaddress based on a narrowed database search associated with thecity(ies) which reside in that state.

Accordingly, analyzing the object information can be instructive inidentifying a process associated with a corresponding pattern.Furthermore, the selected pattern can be instructive in identifying orinterpreting specific fields of object information that otherwise maynot be clear absent correlation with the category information of thepattern both for the field in question and based on the other fields ofthe object. The order that the fields are examined or processed may bedefined in the pattern.

If all, or a sufficient number, of the object information correlateswith the category information, then the directory system may determinethat the selected pattern provides a good match with the mail piece. Thedirectory system may process the object 10 according to instructionsassociated with the selected pattern. On the other hand, if the objectinformation does not sufficiently correlate with the categoryinformation, the directory system selects a next pattern for comparison.

Confidence in a match made with a specific pattern is also aconsideration. There is, not only, a defined order to which patterns areapplied to an object, but also, patterns are assigned a confidence levelthat is reported upon a match. For instance, a match for a mail piecemay be attempted with a pattern that requires an exact match on theCity, State and Zip code, and if this fails a subsequent pattern mayonly require a match on the Zip code. A match on the later pattern whileinstructive may also include a lesser confidence code.

Source code for a sample address pattern is provided as follows:

− <AddressPatterns name=“USA Zip+4 Patterns” version=“1.0”xmlns=“http://www.raf.com/Smi”> − <Pattern name=“Street_Zip”levelOfSort=“255” isFinal=“true” direction=“BottomToTop”> −<AddressBlock> − <Line> <ComponentsearchOrder=“3”>PrimaryNum</Component> <NoiseChars maxQuantity=“2” /><Component searchOrder=“4” isOptional=“true”>PreDir</Component><Component searchOrder=“2” minFuzzyMatchLevel=“Low”allowTransposedWordMatch=“true” allowMatchLastWordFirst=“true”allowMissingMiddleWord=“true” allowTruncation=“true”>Street</Component><NoiseChars maxQuantity=“2” /> <Component searchOrder=“5”minFuzzyMatchLevel=“Low” isOptional=“true”>Suffix</Component><NoiseChars maxQuantity=“2” /> <Component searchOrder=“6”isOptional=“true”>PostDir</Component> <NoiseChars /> </Line> <NoiseLinesmaxQuantity=“1” /> − <Line> <NoiseChars /> <Component searchOrder=“1”requireExactMatch=“true”>Zip5</Component> − <!-- ‘-’ + Zip4 --><NoiseChars maxQuantity=“5” /> − <!-- Delivery point or other post-zipnoise (separate declaration for unit test) --> <NoiseCharsmaxQuantity=“3” /> </Line> <NoiseLines maxQuantity=“3” /></AddressBlock> − <Output> <TransportField>Zip5</TransportField><Field>Zip4Low</Field> <Field>Zip4High</Field> <Field>PrimaryNum</Field></Output> </Pattern> − <Pattern name=“PoRrHc_Zip” levelOfSort=“255”isFinal=“true”> − <AddressBlock> − <Line> <NoiseChars maxQuantity=“2” /><Component searchOrder=“2”allowAbbreviationMatch=“false”>PoRrHc</Component> <NoiseCharsmaxQuantity=“3” /> <Component searchOrder=“3”>PrimaryNum</Component><NoiseChars /> </Line> <NoiseLines maxQuantity=“1” /> − <Line><NoiseChars /> <Component searchOrder=“1”requireExactMatch=“true”>Zip5</Component> − <!-- ‘-’ + Zip4 + DP --><NoiseChars maxQuantity=“7” /> </Line> <NoiseLines maxQuantity=“3” /></AddressBlock> − <Output> <TransportField>Zip5</TransportField><Field>Zip4Low</Field> <Field>Zip4High</Field> <Field>PrimaryNum</Field></Output> </Pattern> − <Pattern name=“Street_CS_Exact” levelOfSort=“250”isFinal=“true” maxNumRecords=“0”> − <AddressBlock> − <Line> <ComponentsearchOrder=“7”>PrimaryNum</Component> <NoiseChars maxQuantity=“2” /><Component searchOrder=“5” isOptional=“true”>PreDir</Component><Component searchOrder=“3” requireExactMatch=“true”>Street</Component><NoiseChars maxQuantity=“2” /> <Component searchOrder=“4”minFuzzyMatchLevel=“Low” isOptional=“true”>Suffix</Component><NoiseChars maxQuantity=“2” /> <Component searchOrder=“6”isOptional=“true”>PostDir</Component> <NoiseChars /> </Line> <NoiseLinesmaxQuantity=“1” /> − <Line> <NoiseChars maxQuantity=“2” /> <ComponentsearchOrder=“2” requireExactMatch=“true”>City</Component> <ComponentsearchOrder=“1” readRightToLeft=“true”>State</Component> <NoiseChars /></Line> <NoiseLines maxQuantity=“3” /> </AddressBlock> − <Output><TransportField>Zip5</TransportField> <Field>Zip4Low</Field><Field>Zip4High</Field> <Field>PrimaryNum</Field> </Output> </Pattern> −<Pattern name=“Street_CS” levelOfSort=“250” isFinal=“true”maxNumRecords=“0”> − <AddressBlock> − <Line> <ComponentsearchOrder=“7”>PrimaryNum</Component> <NoiseChars maxQuantity=“2” /><Component searchOrder=“5” isOptional=“true”>PreDir</Component><Component searchOrder=“3” allowLeadingAttachedNoiseMatch=“false”allowTrailingAttachedNoiseMatch=“false” allowTransposedWordMatch=“true”allowMatchLastWordFirst=“true” allowMissingMiddieWord=“true”allowTruncation=“true”>Street</Component> <NoiseChars maxQuantity=“2” /><Component searchOrder=“4” minFuzzyMatchLevel=“Low”isOptional=“true”>Suffix</Component> <NoiseChars maxQuantity=“2” /><Component searchOrder=“6” isOptional=“true”>PostDir</Component><NoiseChars /> </Line> <NoiseLines maxQuantity=“1” /> − <Line><NoiseChars maxQuantity=“2” /> <Component searchOrder=“2”allowLeadingAttachedNoiseMatch=“false”allowTrailingAttachedNoiseMatch=“false”allowTruncation=“true”>City</Component> <Component searchOrder=“1”readRightToLeft=“true”>State</Component> <NoiseChars /> </Line><NoiseLines maxQuantity=“3” /> </AddressBlock> − <Output><TransportField>Zip5</TransportField> <Field>Zip4Low</Field><Field>Zip4High</Field> <Field>PrimaryNum</Field> </Output> </Pattern> −<Pattern name=“PoRrHc_CS” levelOfSort=“250” isFinal=“true”maxNumRecords=“0”> − <AddressBlock> − <Line> <NoiseChars maxQuantity=“2”/> <Component searchOrder=“3”allowAbbreviationMatch=“false”>PoRrHc</Component> <NoiseCharsmaxQuantity=“3” /> <Component searchOrder=“4”>PrimaryNum</Component><NoiseChars /> </Line> <NoiseLines maxQuantity=“1” /> − <Line><NoiseChars maxQuantity=“2” /> <Component searchOrder=“2”allowLeadingAttachedNoiseMatch=“false”allowTrailingAttachedNoiseMatch=“false”>City</Component> <ComponentsearchOrder=“1” readRightToLeft=“true”>State</Component> <NoiseChars /></Line> <NoiseLines maxQuantity=“3” /> </AddressBlock> − <Output><TransportField>Zip5</TransportField> <Field>Zip4Low</Field><Field>Zip4High</Field> <Field>PrimaryNum</Field> </Output> </Pattern> −<Pattern name=“CSZ_Only” levelOfSort=“10” isFinal=“false”maxNumRecords=“0”> − <AddressBlock> − <Line> <NoiseChars /> <ComponentsearchOrder=“3” allowTruncation=“true”>City</Component> <NoiseCharsmaxQuantity=“2” /> <Component searchOrder=“2”readRightToLeft=“true”>State</Component> <NoiseChars maxQuantity=“2” /><Component searchOrder=“1” requireExactMatch=“true”>Zip5</Component><NoiseChars /> </Line> <NoiseLines /> − <!-- Extra NoiseLines elementfor unit test --> <NoiseLines /> </AddressBlock> − <Output><TransportField>Zip5</TransportField> </Output> <Pattern><AddressPatterns>

The Database

FIG. 3 illustrates a screen shot of a mail processing system's visualdisplay. This display shows an image of a mail piece 30 which hasundergone optical character recognition, with the result then sent tothe directory system 125 (FIG. 1), returning recognized and matched andverified address 35. The mail piece 30 has undergone optical characterrecognition (OCR), directory lookup in the directory system and returnedresult 35. The image of the mail piece 30 includes an address block 25.The address block data identified from the OCR may be displayed as oneor more fields of object data 35 corresponding to which categoryinformation is being analyzed. The object data 35 corresponds to theobject information as matched with or identified using a correspondingpattern or patterns in the database(s).

There are at least two kinds of information in the database: specificinstances of the components of defined patterns and instructions on whatto do with the object when an instance of a defined pattern is found.The components of the defined patterns are listed in the database insuch a way that the parser can tell which defined pattern(s) they applyto. This linkage may be as direct as database elements that assign thecomponents to a particular pattern or patterns (pattern numbers ornames, for example) or it may be implied by the database structure. Acolumn may be headed by a header “Province Name”, for example, withspecific province names repeated for all the cities in the province,neighborhoods in the cities, etc. (which appear in subsequent columns),and any pattern requiring that information about an address would usethe data in those columns.

Entries (typically rows) in the database may relate to one or moredefined patterns. That is, the parser may use the same set of databaseentries to attempt to match more than one pattern. This is particularlytrue when two patterns differ only by an optional item. For example, ifthe “Neighborhood” is an optional address element, one pattern maydirect the parser to look for it in a particular place on the mail piecewhile another ignores the neighborhood and looks for a city name in thesame location on the object. The parser would in this circumstance usethe same database row to try to match the data from the object to thetwo different patterns, and would follow the instructions associatedwith that row once a match was found.

A typical simple example of a mail piece database would have one row foreach delivery point in the country. The row might contain all theelements that would be present were the address complete and in thiscase the entire database would be applicable to all patterns. A morecomplex database might have regions of the database applicable tospecific patterns and not applicable to others. The database would thenhave an indicator, useable by the parser, of which part of the databaseto use for a particular pattern. Different patterns may be associatedwith different databases and different pattern sets may use apply to asingle database.

The database may contain additional information to improve the parser'sability to match text strings contained in the object information.Non-standard abbreviations, transliterations, aliases, and numerictransformations (e.g. “1” to “ONE” or “SIETE” to “VII”) specific to thepattern domain may be included. Word translation lists may be definedfor multilingual applications. Mappings may be specified betweencharacters with accent marks or non-Latin characters and theirtypographic or OCR equivalent. For example, the user may want to treatTV and ‘n’ as equivalent, or ‘ü’ and ‘ue’ as equivalent.

The database allows the parser to determine whether the address blockobject data 25 found on the object matches one of the defined patterns.The parser/database combination may allow matching of more than onepattern and a module for resolving the ambiguity. For example, aneighborhood and a city might have the same name (in Mexico, forexample, it is quite possible to have a delivery point for which thedistrict, city, and neighborhood all have the same name, only some ofwhich appear on the mail piece).

If there are different routing instructions if the pattern matches thatduplicated name to the city or if it matches it to the neighborhood, theparser may attempt to match both patterns and, getting a match to both,pass the results to a module that resolves the ambiguity by only routingthe item to the deepest place in the sort that matches both patterns (inthis case, the city level). The database may also be configured toautomatically provide such decisions by putting the routing code for thecity level of sort as the routing instructions for everycity/neighborhood pairing that is duplicated.

Once a match to a defined pattern has been made, the database providesinstructions on what to do with the object. For a mail piece, thedatabase might include and return a bar code to be sprayed or printed onthe mail piece. Typically, each row in the database is unique and eachone has one set of instructions to be implemented. In many cases theinstructions may be the same for many database rows. For example, acountry that automatically sorts only to neighborhood level and leavesit up to the courier to determine the final delivery may provide adatabase that contains street names and numbers but provides the samerouting instructions for all streets and number ranges within aparticular neighborhood. Multiple matches may be returned.

The instructions provided by the database may be code numbers that therest of the system (e.g. the mail sorting system) knows how to interpretto properly handle the object. The instructions in the directory systemmay include a tracking code for a matched object, Latitude and Longitudecoordinates (FIG. 11) that may be used to further qualify a destinationaddress, or instructions in plain text to a user on what to do with theobject.

The Parser

FIG. 4 illustrates the sequence used when the directory system parses anobject, in this case a “ParseAddress. For sake of clarity, the variousfunctions of the parser 40 are described linearly, however it should beunderstood that one or more of the functions may be performed in adifferent sequence or omitted altogether. Furthermore, it should beunderstood that this is a highly reentrant system, capable of usinginformation deduced in one part of the process to clarify informationobtained in another.

The parser 40 is illustrated as comprising several components, includinga controller 41, configuration manager 43, pattern matcher 45, componentmatcher 47, and arbitrator 49. The parser 40 combines the other threecomponents—the data from the object, the defined patterns, and thedatabase elements—and passes on to the rest of the system theinstructions for handling the object. A primary purpose of the parser isto handle all the inaccuracies and errors that occur in the real world.Thus, in every step of what is described below as the functioning of theparser, it should be understood that the parser may be correcting errorsin the object data or in the database and providing leeway indetermining how well the data from the object matches a given definedpattern.

The parser may supply to the rest of the system not only handlinginstructions, but how confident it is that those instructions arecorrect. The uncertainty measurement by the parser may be used by thesystem (whether for a single information element or for the entire dataextracted from the object) to modify the handling of the object, torequest a new image from the object, to try a different pattern, to callfor manual intervention, or to otherwise modify the handling of theobject.

The parser receives object data 42 from the object, which data isintended by the system to provide information sufficient for determininghow to handle the object, and which in turn identifies categorizinginformation. In a mail piece, the object data comprises a string ofcharacter data that might comprise an address element, whereas thecategory data comprises the X-Y coordinates of each of those characters.Stated another way, the object data provides the directory system withraw data while the category data provides information that allows theparser to determine what kind of information the raw data contains.

The parser has access to the defined patterns and instructions 44 on howto apply them. These instructions 44 may be to apply them sequentiallyuntil a match above a certain confidence is found, to apply them untilall possible matches are found, or almost anything else. In particular,the instructions 44 may tell the order in which to apply the patterns(and under what circumstances to cease applying them) for determininghandling instructions for a given object.

The parser also has access to the database and uses the information inthe database to attempt to fill in the defined patterns with informationextracted from the object. It can fulfill this function in many ways,but the following describes one possible application to a mail piece.The parser has received categorizing information as well as object datafrom the object. It uses this categorizing information 46 to determinewhat pattern data element a piece of object data might represent. Thus afive digit number on a mail piece may be a ZIP code if it appears in thelast line, but a street number if it appears in the second.

One goal of the parser is to fill in all the required elements of adefined pattern with data extracted from the object whose characterizinginformation is within the tolerances specified by the defined pattern.It fills in the elements by determining from the characterizinginformation which data elements the object data might be and determiningfrom the database what specific data element in an actual address (orobject data row) is matched, and how well.

If the parser is able to satisfactorily fill in a defined pattern withdatabase objects of the proper type using object and characterizinginformation from the object, it reads the instructions 48 in thedatabase on what to do with the object and makes that informationavailable to the system. If it is unable to do so (or if itsinstructions tell it to keep working until all possible patterns areexhausted), it goes on to the next pattern and continues until nopatterns remain. If it finds no satisfactory match it has defaultinstructions that it outputs telling the system that no defined patternwas matched satisfactorily.

Smart Matching

FIG. 5 illustrates the overall data and data flow for a directory system50 including both a front-end directory data compiler 52, its associatedinput files and tables, 51, 65, 59, 58, and a back-end runtime patternidentification and the back-end pattern identification andcategorization processing inputs 54. On the left side, the directorydata compiler inputs 52 are what the user puts together in order tocreate the setup for the directory system to run, and the right sideillustrates the input files 54 that are used when the directory systemis being run.

The directory data compiler 52 is provided to ensure that theconfiguration files 51 are well-formed. The directory data compilersystem 52 validates two aspects of the configuration files 51: structureand data content. The configuration file 51 is said to be structurallyinvalid if, for example, it contains improper elements or is missing aclosing tag. The configuration file 51 is said to have invalid datacontent if element or attribute data does not match the type specifiedin the schema. For example, a positive integer value is expected forsearchOrder; a content error would result if the attribute had thestring value “bunny.”

Note that some kinds of logic errors may not be readily detected. Forexample, an address pattern 65 may have used a component name that doesnot match a corresponding address data field name. Additional checkingmay be performed by the directory system (FIG. 1) 125 at run-time tonotify the user of such errors.

Runtime character matching allows for the specification of the name ofthe file that contains mappings to allow the directory system to matchcommon character alternates. For example, ‘Ö’ could exist in thedirectory data 53, but it will likely be recognized using OCR withoutthe umlaut, so the mapping ‘Ö’->‘O’ can be made to improve fuzzymatching performance. In one embodiment, only one CharacterMatchTable 55element is allowed. The character match table file 55 may include aUTF-8 encoded text file, with one mapping per line. A mapping isdeclared with the character found in the directory data 53 on the left,followed by a dash-greater than style arrow sign (“->”), followed by themapped character. The dash-greater than style arrow sign is alsoillustrated herein as a simple right-directional arrow for convenience.

More than one mapping may be declared for the same directory datacharacter, with each character appearing on a separate line. Forexample:

Ö→O

Ö→8

Note that the character match table 55 may be case-sensitive. Thereforecharacter mappings that are meant to apply to non case-sensitive fieldshave upper case values on both sides of the “→”.

Word matching allows for the specification of the name of the file thatcontains mappings to allow the directory system to match common wordalternates. For example, “MOUNT” could exist in the directory data 53 aspart of a field value, but it is commonly abbreviated as “MT”, so themapping “MOUNT” “MT” should be made to improve fuzzy matchingperformance. In one embodiment, only one <WordMatchTable> element isallowed.

The word match table file 56 may be a UTF-8 encoded text file, with onemapping per line. A mapping is declared with the word found in thedirectory data 53 on the left, followed by a dash-greater than stylearrow sign (“→”), followed by the mapped word. More than one mapping maybe declared for the same directory data word, with each characterappearing on a separate line. For example:

MOUNT→MT

MOUNT→MNT

Word match table entries can also be used for numeric input anddirectory data words. For example:

20TH→TWENTIETH

TWENTY→20

An ignorable word option specifies the name of the file that containswords such as articles and prepositions which the directory system canignore in the input string or directory string while fuzzy matching. Forexample, if the ignorable words table contains “OF” and “THE”, then theinput string “AVENUE AMERICAS” will fuzzy match the directory string“AVENUE OF THE AMERICAS”. Similarly, the input string “AVENUE OF THEAMERICAS” will fuzzy match the directory string “AVENUE AMERICAS”. Asmall penalty may be applied to the match score for each ignored word sothat a better score is achieved if the ignorable words are present andmatched. In one embodiment, ignorable words must match exactly; “THF”would not be ignorable if the table only contained “THE”.

The ignorable words table 57 file may be a UTF-8 encoded text file withone ignorable word per line. Leading and trailing whitespace may betrimmed. In one embodiment whitespace is not allowed within the word.

Customer address data 51 consists of address records 58 and alias tables59. Customer address data 51 can be imported into the directory systemfrom a text file that contains either delimited or fixed-width fields.An XML configuration file is used to define the fields to be loadedalong with properties of those fields, and specify the locations of thefields in the data file. Whether fixed-width or delimited, a customeraddress data file 51 is expected to have one record per line, with aline feed character (‘\n’) at the end of each line.

A customer address data configuration file 51, which contains at leastone address file definition and may contain one or more optional aliasfile definitions, is used by the directory data compiler 52 to createthe directory data file 53. Note that the example uses only delimitedaddress and alias files, but both delimited and fixed width files can bemixed in the same configuration.

For fields that are not case-sensitive, values (including aliases) areconverted to all upper case. If an upper case equivalent does not existfor a character then it is not modified. For example:

“Redmond Woodinville”→“REDMOND WOODINVILLE”

“90th”→“90TH”

Character match, word match, and ignorable words may be provided in oneor more tables. In one embodiment, character match, word match, andignorable words are not converted and are always case-sensitive.Therefore values that are meant to apply to non case-sensitive fieldsare given in upper case.

Field aliases can be defined to improve address pattern matching. Analias is an alternate but equivalent representation of data for aspecific field value. For example, “Calif” and “California” aresometimes used as aliases for the preferred, canonical two-letter statecode “CA”. If either “Calif” or “California” is found in an addressblock it may be considered a match to a record that contains thecanonical field value “CA”. Each alias table is tied to a specificfield. So while “Montana” may be an alias for the state field value“MT”, it is not an alias for the word “MT” in the street field value “MTSHASTA”.

Consider the following sample of a delimited table of two-letter statecode aliases. An alias may consist of a single value or a list ofvalues. For example, there might be multiple aliases for a city name. Ifa list of values is used then a delimiter may be supplied to correctlyparse the list.

// Common state aliases // Canonical=Alias AK=ALASKA AL=ALABAMA,ALAAR=ARKANSAS,ARK AZ=ARIZONA,ARIZ CA=CALIFORNIA,CAL,CALIFCO=COLORADO,COL,COLO CT=CONNECTICUT,CONN DC=DISTRICT OF COLUMBIA,WASHDC,WASHDC,DIST COL DE=DELAWARE,DELA,DEL ...

The directory system 125 (FIG. 1) reads addresses by comparing a blockof character strings or recognition results to a set of one or morecustomer-defined address patterns 65. The address pattern 65 describeswhere different components of an address, such as street name andpostcode, can be found relative to one another, and relative to theaddress block containing them. In addition, the address pattern 65defines areas where ignorable text (“noise” which is not an importantpart of the address) may be found.

FIG. 6 graphically represents an example pattern 64 comprising anaddress block 62. The example pattern 64 may be used to describe theaddress block 62 using field descriptors 64. In this pattern 64, city,state, and suite number are treated as noise 66. In another applicationit might be preferable to identify these components in the patternrather than ignore them. The firm line is ignored in this case with orwithout a noise declaration because this pattern 64 is searched frombottom to top and the firm line is above the topmost line that containsrequired components.

An input address block may contain additional information, such astelephone number or addressee name, mixed with required component data.To improve pattern matching performance with this extra data, noisecharacter placeholders can be declared in the pattern. Up to maxQuantitycharacters in the input address block can be ignored between twomatching components if a <NoiseChars> element exists in the patternbetween the two <Component> elements. To declare that one or more entirelines of the input address block could be ignored as noise a<NoiseLines> element should be used.

<NoiseChars> and <NoiseLines> may contain element text making it “namednoise”. This text indicates that the content of this specified noisearea should be written as output using the given element text as thename. Specification of named noise adds the following restrictions tothe noise elements:

-   -   No consecutive <NoiseChars> or <NoiseLines> where 1 of the        elements is named    -   No <NoiseChars> or <NoiseLines> with only optional components        between them (including all optional lines)    -   No completely optional lines with a named <NoiseChars> element

Patterns may be configured using an XML file. The Pattern file 65 (FIG.5) contains one or more patterns, each identified by a customer-definedname. The directory system attempts to match each pattern in the orderin which it appears in the file. In one embodiment, a pattern 64 definesa single configuration of an address block 62 and the fields 64 thatwill be returned to the caller for each address record that successfullymatches the pattern 64.

A line represents a single line of an address as it appears on a pieceof mail. In one embodiment, a line must contain at least one component,and can also contain noise. The position of each <Line> element in theAddressBlock 62 is important since the pattern matcher utilizes the samerelative positions in the input data.

A component is a piece of an address, such as postcode or street name,which is represented by a field in the directory data. <Component>elements are defined for a line in the same order in which they areexpected to appear in the input data.

The pattern matcher can determine in what order it should search theinput data for matching component values. Some components are consideredoptional and an address record will not be rejected if it has acomponent value that was not found in the input. For example, if streetsuffix is optional, then the input “MAIN” will match the record “MAINST”. Given two records that are identical except one has a matchingoptional component and the other does not, the one with the matchingoptional component is preferred. For example, given the input “MAIN ST”,the record “MAIN ST” is preferred over “MAIN RD” and simply “MAIN” ifstreet suffix is optional.

The component matcher can scan each line from right to left looking forthe component instead of left to right. This can improve matchingperformance for components that are typically found on the right side ofthe line, such as postcode.

Each character in the directory data string must be represented by acharacter in each possibility set of the text or OCR string. If allvalues for the field have one word and the same number of characters,the matcher is able to handle a limited number of split/merge cases(i.e. directory data string is split into two or is merged with anotherstring in the input). A single missing, leading zero digit is allowedfor fields that are all numeric. For example, the directory data string“08010” matches the input string “8010”.

A fuzzy logic matching confidence threshold can be set for thecomponent. String fuzzy matching is used to compare the input to fieldvalues from the directory. Allowed settings are: “VeryLow”, “Low”,“Default”, “High”, and “VeryHigh”. A better match may be required for“High” than for “Low”. For example, the directory string “WOODINVILLE”would not match the input string “WODNVALLE” with a setting of“VeryHigh”, but it would match with a setting of “VeryLow” or “Low”.

The pattern matcher may match directory words to input abbreviations.For example, with this feature enabled the directory string “JohannSebastian Bach” would match the input “J Sebastian Bach” or “J S Bach”.At least one word must be unabbreviated, so “J S B” would not be anacceptable match. [Optional, default=true]

The pattern matcher may match directory words to an input acronym, orvice versa. For example, with this feature enabled the directory string“Salt Lake City” would match the input “SLC”. Similarly, the directorystring “MLK” would match the input “Martin Luther King”. Ignorable wordsmay be dropped from the directory string, so the directory string“United States of America” would match the input “USA” if “of” is in theignorable words table.

The pattern matcher may match directory words that include contractionsof articles and prepositions to an expanded equivalent form in theinput. For example, with this feature enabled the directory string“Comte d'Urgell” would match the input “Comte de Urgell”. In addition,the input would be matched if the article or preposition is missing, asin “Comte Urgell”.

The pattern matcher may trim leading noise glyphs from an input word inorder to match a directory word. For example, with this feature enabledthe directory string “Dallas” would match the input “IIIDallas”. Asubstring match of a numeric part of a string is not allowed. So whilethe directory string “82nd Avenue” would match the input string “A82ndAvenue”, it would not match the input string “182nd Avenue”.

The pattern matcher may match the last directory word to the first inputword, and then match the remaining words in the proper order. Forexample, with this feature enabled the directory string “San GerolamoEmiliani” matches the input “Emiliani San Gerolamo”.

The pattern matcher may allow a match with the first directory wordmissing from the input. For example, with this feature enabled thedirectory string “Giuseppe Verdi” matches the input “Verdi”. This optioncan be combined with allowMissingMiddleWord,allowMissingNonNumericLastWord, and allowMissingNumericLastWord, howeverno more than one word can be missing from a string.

The pattern matcher may allow a match with any single word from thedirectory string to be missing from the input except the first or lastword. For example, with this feature enabled the directory string “DonLuigi Milani” matches the input “Don Milani”, however “Don Luigi AlbertoMilani” does not. This option can be combined withallowMissingFirstWord, allowMissingNonNumericLastWord, andallowMissingNumericLastWord, however no more than one word can bemissing from a string. Note that a word that is in the ignorable wordstable may not count as missing in this context.

The pattern matcher may allow a match with the last directory wordmissing from the input provided the word does not contain any digits.For example, with this feature enabled the directory string “Cernuscosul Naviglio” matches the input “Cernusco” (assuming “sul” is anignorable word); however “State Route 20” does not match “State Route”unless allowMissingNumericLastWord is also enabled. This option can becombined with allowMissingFirstWord, allowMissingMiddleWord, andallowMissingNumericLastWord, however no more than one word can bemissing from a string.

The pattern matcher may allow a match with the last directory wordmissing from the input provided the word contains digits or is a Romannumber. For example, with this feature enabled the directory string“Vittorio Emmanuele II” matches the input “Vittorio Emmanuele”; however“Martin Luther King” does not match “Martin Luther” unlessallowMissingLastWord is also enabled. This option can be combined withallowMissingFirstWord, allowMissingMiddleWord, andallowMissingNonNumericLastWord, however no more than one word can bemissing from a string.

The pattern matcher may trim the numeric ordinal, i.e. “st”, “nd”, “rd”,or “th”, from a directory word prior to matching the input word. Forexample, with this feature enabled the directory string “29th Ave” wouldmatch the input “29 Ave”. To avoid false-positives, ordinal trimming isnot attempted with directory words that have a single-digit numericportion, so the directory string “5th PI” would not match “5 PI”.

The pattern matcher may match a Roman number to a numeric string. Forexample, with this feature enabled the directory word “XXIII” matchesthe input “23” and the directory word “23” matches the input “XXIII”. Anexact match may be required.

The pattern matcher may trim trailing noise glyphs from an input word inorder to match a directory word. For example, with this feature enabledthe directory string “Elm Street” would match the input “Elm StreetIII”.A substring match of a numeric part of a string is not allowed. So whilethe directory string “Highway 52” would match the input string “Highway52A”, it would not match the input string “Highway 521”.

The pattern matcher may match strings with pairs of words transposed.For example, with this feature enabled the directory string “RedmondWoodinville Road” would match the input “Woodinville Redmond Road”. Eachword may only be affected by a single transposition. So the directorystring “Redmond Woodinville Road” would not match “Woodinville RoadRedmond” because two transpositions would be necessary:“Redmond”/“Woodinville” followed by “Redmond”/“Road”.

The pattern matcher may match an input word with a truncated directoryword. For example, with this feature enabled the directory string“Philadelphia” would match the input string “Phila”. Where truncation isonly allowed on the right-hand side, “Philadelphia” would not match“Delphia”.

FIG. 7 illustrates a process 700 of identifying, imaging, matching,verifying classifying and delivering the results of an object match. Forexample, an object such as the image of a label of address may beprocessed. At operation 705, an object or mail piece is loaded into thedirectory system. In one embodiment, a large number of mail pieces areloaded into the directory system at the same time for sequential orparallel evaluation of the mail pieces.

At operation 710, an image of the object is obtained. The image may beobtained with a camera, scanning device (e.g. charge coupled device CCDor contact image sensor CIS), optical sensor, thermal imaging device,magnetic imaging device, etc. In one embodiment, images of the objectsare uploaded to the directory system in bulk. For example, the objectsbeing identified, sorted, delivered, or classified may have beenpreviously scanned or photographed ahead of time.

At operation 715, the image of the object is processed with arecognition system, such as a system which utilizes OCR. The recognitionsystem may parse the image into separate lines of characters or wordsthat may be analyzed for context and/or meaning. The recognition systemmay identify an address block of the image, which specifies an intendeddestination of the object or mail piece.

At operation 720, the parsed image data is compared with a firstpattern. For example, a first line of the address block is compared witha first field or component of the first pattern. Similarly, a secondline of the address block may be compared with a second field of thefirst pattern. In one embodiment, a single line of the address block maybe associated with, or compared to, a plurality of fields in thepattern. Operationally this matching may be performed top down or bottomup.

The patterns may be weighted. The weightings may determine an order forcomparison of the patterns with the image data. For example, the firstpattern to be compared to the image data may have a higher weightingthan a second pattern to be compared to the image data.

At operation 725, a confidence level of the comparison of the image datawith the first pattern is determined. A confidence threshold may beassociated with the first pattern. In one embodiment, the first patternis validated, or considered a match, when the confidence level equals orexceeds the confidence threshold for the first pattern.

If the confidence level is less than the confidence threshold associatedwith the first pattern, the directory system may then compare the imagedata with the second pattern according to the assigned weighting of thepatterns. A confidence level of the comparison of the image data withthe second pattern may then be determined. The remaining patterns may becycled through until a confidence threshold of a corresponding patternis met or exceeded.

At operation 730, a pattern is selected or validated for the image data.In one embodiment, a single pattern is selected for the image data. Forexample, as soon as the confidence threshold for the correspondingpattern has been met or exceeded, the directory system stops cyclingthrough the plurality of patterns and selects the corresponding pattern.

At operation 735, a pattern output is identified for the selectedpattern. The pattern output may identify a standard or canonical formatcorresponding to the address block. In one embodiment, the canonicalformat provides additional information that was not included in theaddress block. The canonical format may also replace one or more wordsin the address block with a more standardized version, or correctedspelling. The canonical format may also remove redundant or unnecessaryinformation that was identified in the address block. In one embodiment,the output does not include any of the same information that is includedin the address block, but rather points to related information in adatabase such as gee coordinates, a telephone number, or a bar code.

At operation 740, the canonical format is sprayed on, printed on, orotherwise applied to the object or mail piece. In one embodiment, thecanonical format information is transferred to the object via ashort-range signal such as RFID. In that case, the object may include amemory chip which is configured to store the canonical formatinformation. The canonical format information may then be used forfurther sorting, delivery, classification, or inventory of the object.

FIG. 8 is an example screen shot of the mail processing system's visualdisplay. It illustrates the use of the directory system to returnstandardized output (86 and 88) based on matching and verifying anaddress (82). Based on location or pattern matching as described above,an address block 82 is identified from the image 85. The address block82 may include the destination of a mail piece, or an identification ofan object being inventoried, for example.

The directory system includes multiple selection tabs 84 that includeoptical character recognition (OCR), system snapshot, statistics,alerts, and all statistics. In the present screen shot illustrating theall statistics selection tab 84, a mail sort job is summarized asincluding 16649 mail pieces, of which those that have been processedinclude 16423 with finalized pattern matches, and two with partialpattern matches.

The right side of the screen shot includes a directory resultidentification 88 which identifies that a pattern match has been madewhich identifies the image as corresponding to directory result 54360. Apartial or complete identification of the directory result 54360 isprovided in the address result box 86. In the example provided, theinformation shown in the address result box 86 matches, or nearlymatches, the last line of the address block 82. The abbreviation forMexico is provided as MEX in the address block 82, whereas it is fullyidentified as “Mexico” in the address result box 86.

Database Optimization

The directory system may be used in applications such as high-speed mailsorting where response time is critical. The directory system is alsodesigned to accommodate user data consisting of an arbitrary collectionof fields, so database optimization must be able to automatically adaptto the data and patterns related to a specific application.

The directory system optimizes database access at compile time, whenuser data is normalized, analyzed, and loaded into a binary file format,and at initialization time, when software using the directory system isstarted. At compile time the directory system performs adaptive indexingto improve database query speed. By analyzing the data, patterns, anduser configuration, the directory system determines which fields shouldbe indexed to balance performance with database size. Indexes cansignificantly increase the database size, so it is not practical tocreate indexes for all combinations of fields. For example, consider adata set consisting of US addresses. The following pattern could bedefined to match a complete address:

-   -   State→City→Street→House        Number→Suffix→Pre-directional→Post-directional (fields are shown        in search order)

An index is created for state since the pattern matcher will need thelist of city values associated with the parsed state. Similarly, atwo-field index for state-city is created because the pattern matcherwill retrieve a list of streets associated with the previously parsedstate-city combination. A three-field index, state-city-street, is alsocreated, but since the number of records associated with a specificstate-city-street combination is relatively small this will be the lastindex created for this pattern. At this point entire records would befetched instead of values for a single field.

At initialization the directory system analyzes the patterns andpreemptively caches data that will be queried frequently or may be tooexpensive to access at parse-time. Given the US address patterndescribed above, for example, the directory system knows to query thedatabase to generate a static list of all state values to be used by thepattern matcher. The directory system then analyzes the size of the listof state values. If the list is not too long, the directory systemqueries the database to create a static, associative table of cityvalues for each state value. These static data structures can be ordersof magnitude faster to access and manipulate compared the sameoperations with a SQL query.

EXAMPLES

A Specific Address Use Case may include a single data base and a set ofpatterns to correlate a result with context information. Iceland mail isformatted as follows.

-   -   “Person” or “Firm Name”    -   “Street Name” “House Number”    -   “Postcode “City Name”    -   “Country”

For routing in smaller cities you typically need “Postcode” and “CityName” (cities with only one Postcode) and for larger cities you need“Street Name” and sometimes “House Number” (where a street could be inmultiple nine digit postcode areas), “Postcode” and/or “City Name”. Ineither case, the return is a nine digit “Post code”. Addresses only havethree digit “Postcodes”. For Example, when considering this Icelandicaddress:

-   -   Lára Borg Ásmundsdóttir    -   Laugateigi 20    -   105 REYKJAVIK

This routes to 10500250000 if you route it with postcode, city, andstreet name. However if you specify a route based only on “Postcode” and“City”, this address may not be verifiable because it is a “Large” citywhere street and house number may be required by the Directory. However,when considering this one:

-   -   Flug        jónustan Keflavikf    -   V.HEITI: IGS—REST    -   KEFLAVÍKURFLUGVÖLLUR    -   235 KEFLAVÍKURFLUGVÖLL

This address will finalize and route by “Postcode” and “City” becauseit's from a smaller city and this routes to 235000000.

FIG. 9 illustrates the mail processing system using the directorysystem's fuzzy logic to return verified and matched information (86)associated with an image of an address (92). In this example, theaddress block includes a third line of text as “C/ PAU CLARIS 161, 3o2n” and a fourth line of text as “08037 BARCELONA”.

The third line of text may be understood to be a short-hand orabbreviated form of the street address that may not be identical with adirectory entry. The directory system may initially query the directoryto locate partial matches to one or more of the words “PAU” or “CLARIS”or that include the number “161”.

The zip code and country (e.g. 080307 BARCELONA) on the other hand, mayprovide an exact match in the directory. Upon finding one or morematches with the street address, the directory system may then comparethose records with valid street addresses that are associated with thevalidated zip code and country. In the directory result box 86 of theexample provided, the directory system has validated “CALLE DE PAUCLARIS 161” as being associated with the third line in the addressblock.

Validation of the street address may be accomplished by comparing storedstreet addresses for the validated zip code and country, and byverifying a confidence level of the match of the scanned street addresswith the stored street addresses. In one embodiment, the zip code andcountry is validated before searching for the street address match tofurther narrow the number of initial directory results prior toperforming the fuzzy logic match.

Canonical Address Translation

FIG. 10 is an example screen shot of the mail processing system's visualdisplay. It illustrates the use of the directory system's aliasingcapabilities to associate and match toan alias with informationidentified on an image of an address (102). The third line of theaddress block 102 of the image data appears as “260 Njar

vík”. In an example embodiment, the directory may not include an exactmatch with this older name of the town that has since been renamed asReykjanesbaer. In other words, “260 Njar

vík” may no longer be a valid address. However, by including an aliastable, old or local versions of the names of towns may be associatedwith the standardized or canonical version of the address so that themail piece may nevertheless be properly identified, sorted, delivered,or classified.

In the illustrated example, the relationship between the proper name andthe aliases of the names may be stored or referenced via sharedinformation on the Internet as provided in OCR box 106. Upon identifyingthe standardized version of the town's name, the directory system maythen revise the address of the mail piece to replace the older or localversion of the name of the town in order to generate the canonicaladdress 104.

This same concept may be utilized, for example, for sorting and deliveryof international mail pieces. In additional to historical reasons wherethe names of cities may be different from one another, names of citiesand countries may be spelled quite differently simply as a result of thevarious languages involved. An alias table which identifies all of thedifferent variations for the various languages can thereby associate thevariations with a canonical version of the name. For example, thecanonical version of the name of a country may be the version that isnative to the country in question, whereby all other deviations of thatname that are typically used in other countries are associated asaliases. In another embodiment, the canonical version of all the namesare those associated with the versions adopted by a particular acceptedlanguage, such as French.

In the European Union, a name of a city or country may be spelled quitedifferently depending on the language of choice. By way of example, TheNetherlands is commonly referred to by a number of different namesincluding Holland and Les Pays-Bas, to name a few. By associating thesenames with a canonical version of the address, whether a letteraddressed to Amsterdam originated in France or in the U.K. astandardized address or label may be sprayed, printed, or otherwiseapplied to the letter which would identify the same canonical name forthe destination country. By extension, a single canonical address can beapplied for all of the different variations that exist either byconvention or due to differences in languages.

By way of a further example, a mail piece from the United States comesinto China, written in English. It is written to correspond to one ofseveral possible Chinese address templates where the components are allin English. The canonical output format, however, is not in English atall, but in Chinese. The directory system templates and translation tocanonical address formats can be used to automatically translate theaddress from one language (and is associated templates) to anotherlanguage, including the canonical template for that country. Inoperation, a sequence of English-language templates are applied to themail piece and once the template components are filled in, the addresscode for delivery is extracted from the database. The delivery code maythen be sprayed, printed, or otherwise applied to the mail piece forrouting using bar code readers.

A format translation database derives a delivery code by applyingtemplates which point to the canonical form of the address. The processmay work for part of an address or the entire address. For example, themail piece may identify a city but not a state. The template canidentify a unique city without any reference to the state, and outputboth the city name and the state as the official format for applicationon the mail piece. Similarly, the directory system can identify thecanonical address written in a different language from that identifiedon the mail piece as originally received.

FIG. 11 is an example screen shot of the mail processing system's visualdisplay. It illustrates the identification of geo-positional data 118related to the data matched on the image from a scanned address 112,113. In the illustrated example, the GPS coordinates 118 correspondingto the address in the address block 112 are displayed in the OCR box116. In addition to the GPS coordinates 118 a visual map of thedestination address may be displayed. The GPS coordinates 118 may bedisplayed with the canonical address 114 that is associated with theimage data obtained from the address block 112. In addition to geopositional or GPS coordinates, other information associated with thedestination or the person/people located there, such a phone number, maybe displayed or otherwise matched to the image data 115.

Handwriting Recognition

A handwriting engine is highly reliant on contextual information inorder to properly read text. Contextual information allows the engine tobe able to resolve ambiguous words by being able to apply a dictionaryof possible words to a written word. The directory system may be used asthe contextual system for a handwriting engine. The directory systemallows the user to create a parsable structure for text and anassociated dictionary for each of the fields in that block of text. Forexample, a US address block contains different fields that need to beread (city name, ZIP code, street name, etc.) and those text fields arein a small set of possible locations. As another example, a personalcheck has a set of fields that are to be read (date, amount, recipient,etc.) with those fields in a specific set of locations.

Handwriting applications would normally be written for a specific usagescenario without the ability to easily reuse the handwriting engine in anew situation. With the directory system a handwriting engine can easilybe used in new situations by changing the configuration and/or changingthe data dictionary. Effectively, this makes the handwriting engine theparser described above, using the patterns and the database to read thehandwritten text.

A configuration is created that specifies the set of possible layouts oftext elements that are to be read by the handwriting engine. Each textelement in the configuration is associated with a data dictionary. Thedictionary for each element may be dependent on previous elements (forexample, list of streets in a particular city). The dictionary may alsobe a regular expression (for example, a date field on a check). Thehandwriting engine reads the configuration.

Given an input image containing handwritten text, the engine iteratesthrough the words in the text block and while reading, determines whichof the set of configured layouts this input text best matches. As itreads each input text element, it uses the dictionary for that elementin a given text layout to determine how well that layout matches theinput text block.

Automatic Pattern Generation

FIG. 12 illustrates an example process of automatically generatingpatterns from a number of sample objects. At operation 205, a set ofsample input data images are loaded into the directory system. Forexample, the sample input data images can be similar to thoseillustrated in one or more of FIGS. 2, 3, and 8-11. The images can beloaded as digital images, or the images can be obtained from a sampleset of physical objects, which may be scanned using an camera device.

Each input data image will contain input data corresponding to the InputImage Description in a specific defined area of interest. The area ofinterest is meant to represent a standard area of interest that may beencountered in the processing of like input data images.

At operation 210, descriptions of the images are entered or loaded tothe directory system. The descriptions may, but is not required to,identify a location of specific fields or components to provide a set ofspatial identifiers. For example, one location of an address block maybe described as “country”, whereas another location of the address blockmay be described as “street address”. Different patterns are associatedwith a different set of descriptions or spatial identifiers.

The sample input data identifies the pattern name that is associatedwith a specific input image, the priority and/or confidence level ofthis pattern, the pattern field element names, the corresponding datafound in each pattern field element on the image area and in the area ofinterest, and the specific outputs that should be associated with amatch on a given image pattern.

At operation 215, a number of patterns are generated based on the imagesand associated descriptions. A different pattern may be generated foreach image and image description combination. The patterns correlate theimage data with the associated description and optional spatialidentifiers. The patterns may be associated with one or more aliases forcertain of the address fields or components.

For example, the customer creates N images, where each image shows asingle representative example of a typical image to be processedincluding an example of a typical data format with data in the typicallocations. When the set of N images are taken together they representthe entire collection of data formats that the customer system processesusing the patterns to be created. These N images have specific data onthem. This data would be identified in an accompanying text image datadescription file. The data description file includes the image name, thedata that should be found on that image and the ‘meta-tag’ that thisspecific data item is associated with. For instance if the image says:“Blackwood, N.J.” on it, for this image the image data description filemight specify:

<City>Blackwood</City> <State>NJ</State>

The Data Description file may also specify the pattern outputs, patternweighting and other characters of each data item specified. At operation220, pattern outputs are specified for each of the patterns. The patternoutput may identify what information will be sprayed, printed, orotherwise applied to the mail piece if the associated pattern provides amatch. In one embodiment, the pattern output identifies a canonicaladdress.

At operation 225, the patterns are weighted. For example, a firstpattern may be weighted higher than a second pattern. The patternweighting may indicate a preferred or standard format for the addresses.In one embodiment, the pattern weighting relates to a confidence levelin how complete the information associated with the pattern is. Forexample, a pattern which identifies both city and state may have ahigher weighting, or confidence, than a pattern which only identifiesthe city.

At operation 230, confidence thresholds are specified for the patterns.The confidence threshold may identify to what degree the image data ofthe scanned mail piece must match a particular pattern before a match isdetermined or verified. If the confidence threshold for a pattern is notmet, then the directory system moves on to the next lower weightedpattern to determine if a match with the next pattern can be met. In oneembodiment, the directory system stops comparing the image data with thepatterns once the confidence threshold for a corresponding pattern ismet. In this way, the highest weighted pattern which is validated forthe mail piece is selected as a match, or as a best match with the imagedata. Different patterns may have different confidence thresholds.

This system takes the loaded information and generates a plurality ordeck of patterns based on the set of sample input data images and thesample input data image descriptions described above. This includesgenerating a single pattern based on each image and its correspondingdescription and specifying a confidence matching threshold for eachpattern. The specified confidence threshold identifies to what extentimage data must match a particular pattern before a match is verifiedand declared, the spatial relations of the pattern field elements, andthe generic canonical and/or custom output to be associated with a matchon this pattern.

FIG. 13 illustrates an automatic pattern generation system 270. No onehas to actually deduce the patterns by hand. The system 270 provides theability to extract the patterns automatically from such a labeled anddescribed set of images, whether the patterns are simple or complex.

The automatic pattern generation system 270 enables a customer to startwith a set of sample input data images 261 and the description 260 ofwhat's on each of these images, using the system to combine these togenerate the patterns that could be used to match other images that havethe same format as the sample input images. A one-time data file/patternfile generation process may be followed by a runtime phase where thesystem and the data file and patterns are used to recognize input andgenerate specified outputs.

The automatic pattern generation system 270 functionality auto-generatesaddress patterns based on a set of customer supplied images ofrepresentative addresses 261 and a customer supplied corresponding imagedescription file 260 that describes what is on each image. The patternauto-generation process may be run once (or a limited number of times),using the customer supplied images 261 and image description files 260to generate patterns 263 that will subsequently be used as input to thepatented system as it processes images of this type.

Consider the following address block retrieved or scanned from a inputimage 261 mail piece:

SAINT ANDREWS CONVENT

8 BERGEN COURT APARTMENT 2A

BAYONNE NJ 07002

A corresponding Image data description data 260 for the above addressmight include the following source code:

<Component value=796″>PrimaryNumber</Component> <Componentvalue=BERGEN>StreetName</Component> <Componentvalue=07002>Zip</Component> <Component value=BAYONNE>City</Component><Component value=NJ>State</Component> <Componentvalue=COURT>Suffix</Component> <Componentvalue=2A>SecondaryNumber</Component> <Componentvalue=APARTMENT>SecondaryName</Component> <Component value=SAINT ANDREWSCONVENT>RecipientName</Component>

When a specific set of images and a specific set of image datadescriptions are run through the automatic pattern generation system270, the resulting pattern file may be used to aid in optimizing a datafile specific to these reading these types of images and also used inrecognizing other images containing similar data patterns. In oneembodiment, the user can specify the patterns to be evaluated, theconfidence associated with each pattern, and the corresponding outputsfor the selected patterns.

A physical object is provided with enough information on it to allow thesystem 270 to determine and perform a desired function. For a mailsystem this may be an envelope with some attempt at or approximation toan address on the envelope. For a manufacturing plant or parts depot,this may be a label or serial number which identifies a part orotherwise associates information with the part. The system 270 isconfigured to extract the information from the object (objectinformation) and then determine information about that information thatis extracted from the object (categorizing information). For a mailpiece, this additional component may comprise an address block locator234 and an OCR system 233

A defined pattern or set of patterns may exist a priori (e.g. aUniversal Postal Union-defined address format for each country), or itmay be defined for a specific application by a vendor or by a customer.This will be described in detail below. Part of the defined pattern mayinclude information on how to apply the pattern either alone or in adefined and prioritized order with other defined patterns, and whatgeneric and specific information to return.

The database contains the lists of classification elements, individualapplicable element values and the desired system output when a desiredpattern has been matched. For a mail application this database maycontain, for example, a list (the list being a classification element)of states (the states being individual applicable element values), thecities within each state, the neighborhoods within each city, and thecarrier routes within each neighborhood. The desired output may be therouting ZIP code. The database hierarchy corresponds to the classifyingelements to be found on the object and to the patterns created forclassifying the object.

The parser determines which input data fields on the object correspondto which elements in the defined patterns and to which elements andelement values in the database. The parser is smart enough to do fuzzymatching on the input data fields and to interpolate missing elementswhere possible.

The relationship between the defined pattern and the elements in thedatabase may be viewed as similar to that between a defined class in,say, C++ and the many possible instantiations of that class. The patternor patterns show the overall structure and interrelationships of objectelements, while the database provides specific examples, element values(usually meant to be fairly all-encompassing) of those patterns. Thusthe pattern might include “city name” and the database “New Orleans”,“Jackson”, “Sioux Falls” which are examples of city names that might befound on an envelope.

The systems and apparatus illustrated in FIG. 1 and FIG. 13 may beunderstood to correspond with, or provide functionality for, thesystems, apparatus, methods, and processes described previously in thespecification, for example those illustrated in any or all of FIGS.2-12.

One or more of the embodiments illustrated herein provide for thefollowing features:

-   -   High accuracy address lookups based on your list of possible        incoming or outgoing addresses    -   Flexible configuration through address block patterning. The        customer may specify the format(s) of addresses that they, the        customer, want to match to.    -   Read address from the bottom up or the top down.    -   Fuzzy match; auto-correction for; misspelling, abbreviations,        aliases, missing words, specific letter associations,        abbreviated words, ignorable words and phrases, transposed        words, missing words, noise words, and roman numerals.    -   Unicode support for multiple languages.    -   Support for numeric and alphabetical ranged addresses.    -   Flexible configuration through output specification. The        customer may specify the output to be returned on a match and on        a non-match. It is not even required that output be found on the        mail piece. For instance, the customer might configure the        directory system to match to a specific address and return the        GPS coordinates or just the post-code of that address.    -   Specify a ‘level of sort’ to be returned, indicating your        confidence in the completeness of an address matched.    -   Allow for multiple matches where an address is ambiguous or no        match at all in that situation.    -   Address Lookup-up utility that enables you to validate test        addresses and real address without having to run a mail piece        through your sorter.    -   Eliminates Character Noise with reduces errors.    -   Differentiates multiple aliases for a name (e.g. abbreviations        or historic names).    -   Flexible search rules that can define multiple address block        patterns.    -   Provides an address & reverse look-up utility which can debug        routing issues.    -   Output data can be customized (e.g. Postcode, standard address        carrier route, or GPS).    -   Supports outgoing & incoming applications.    -   Includes a Std. VES interface.    -   Fully integrates all domestic and foreign Postal Authority data.

The system and apparatus described above can use dedicated processorsystems, micro controllers, programmable logic devices, ormicroprocessors that perform some or all of the operations. Some of theoperations described above may be implemented in software and otheroperations may be implemented in hardware.

The processor can execute instructions or “code” stored in memory. Thememory may store data as well. A processor may include, but is notlimited to, an analog processor, a digital processor, a microprocessor,multi-core processor, processor array, network processor, etc. Theprocessor may be part of an on-board vehicle control system or systemmanager, or provided as a portable electronic device capable ofinterfacing with the vehicle control system either locally or remotelyvia wireless transmission.

The processor memory may be integrated together with the processor, forexample RAM or FLASH memory disposed within an integrated circuitmicroprocessor or the like. In other examples, the memory comprises anindependent device, such as an external disk drive, storage array, orportable FLASH key fob. The memory and processor may be operativelycoupled together, or in communication with each other, for example by anI/O port, network connection, etc. such that the processor can read afile stored on the memory. Associated memory may be “read only” bydesign (ROM) by virtue of permission settings, or not. Other examples ofmemory include but are not limited to WORM, EPROM, EEPROM, FLASH, etc.which may be implemented in solid state semiconductor devices. Othermemories may comprise moving parts, such a conventional rotating diskdrive. All such memories are “machine readable” in that they arereadable by a processor.

As explained above, the present invention may be implemented or embodiedin computer software (also known as a “computer program” or “code”).Programs, or code may be stored in a digital memory that can be read bythe processor. We use the term “computer-readable storage medium” (oralternatively, “machine-readable storage medium”) to include all of theforegoing types of memory, as well as new technologies that may arise inthe future, as long as they are capable of storing digital informationin the nature of a computer program or other data, at least temporarily,in such a manner that the stored information can be “read” by anappropriate processor. By the term “computer-readable” we do not intendto limit the phrase to the historical usage of “computer” to imply acomplete mainframe, mini-computer, desktop or even laptop computer.Rather, we use the term to mean that the storage medium is readable by aprocessor or any computing system. Such media may be any available mediathat is locally and/or remotely accessible by a computer or processor,and it includes both volatile and non-volatile media, removable andnon-removable media.

Where a program has been stored in a computer-readable storage medium,we may refer to that storage medium as a computer program product. Forexample, a storage medium may be used as a convenient means to store ortransport a computer program.

For the sake of convenience, the operations are described as variousinterconnected functional blocks or diagrams. This is not necessary,however, and there may be cases where these functional blocks ordiagrams are equivalently aggregated into a single logic device, programor operation with unclear boundaries.

Having described and illustrated the principles of the invention in apreferred embodiment thereof, it should be apparent that the inventionmay be modified in arrangement and detail without departing from suchprinciples. We claim all modifications and variation coming within thespirit and scope of the following claims.

We claim:
 1. A method, comprising: obtaining, by a processing device,image data of an object; comparing, by the processing device, the imagedata with a pattern stored in memory, wherein the pattern identifiesspatial information of corresponding pattern elements; determining, bythe processing device, a confidence level of the comparison of the imagedata according to a success in matching the image data with the patternelements; comparing, by the processing device, the confidence level witha confidence threshold associated with the pattern, wherein a pluralityof patterns stored in memory are associated with correspondingconfidence thresholds; and identifying, by the processing device, thepattern from the plurality of patterns based, at least in part, oncomparing the confidence level with the confidence threshold, whereinthe object is processed according to the identified pattern.
 2. Themethod of claim 1, further comprising: comparing, by the processingdevice, the image data with the plurality of patterns; and determining,by the processing device, a plurality of confidence levels based oncomparing the image data with the plurality of patterns, whereinidentifying the pattern comprises identifying the pattern correspondingto a highest confidence level.
 3. The method of claim 1, furthercomprising: comparing, by the processing device, the image data with theplurality of patterns; determining, by the processing device, aplurality of confidence levels based on comparing the image data withthe plurality of patterns; and comparing, by the processing device, theplurality of confidence levels with the corresponding confidencethresholds, wherein identifying the pattern comprises identifying thepattern associated with the corresponding confidence threshold that isfirst met by comparing the plurality of confidence levels.
 4. The methodof claim 1, wherein the plurality of patterns are priority weighted, andwherein identifying the pattern comprises: comparing, by the processingdevice, the image data with the plurality of patterns; determining, bythe processing device, a number of patterns associated with thecorresponding confidence thresholds that are met by comparing the imagedata; and identifying, by the processing device, the pattern from thenumber of identified patterns having a highest priority weighting. 5.The method of claim 1, wherein the object comprises a manufactured part,and wherein the image data comprises an image of a label or a serialnumber associated with the manufactured part.
 6. The method of claim 5,wherein the object is processed as part of an operation associated withmonitoring an inventory of manufactured parts.
 7. The method of claim 5,wherein processing the object comprises identifying a usage of themanufactured part.
 8. A system, comprising: an imaging device configuredto generate image data responsive to a portion of an object locatedwithin view of the imaging device; a memory device configured to store aplurality of patterns; and a processing device configured to: comparethe image data with a pattern stored in the memory device, wherein thepattern identifies spatial information of corresponding patternelements; determine a confidence level of the comparison of the imagedata according to a success in matching the image data with the patternelements; compare the confidence level with a confidence thresholdassociated with the pattern, wherein the plurality of patterns stored inthe memory device are associated with corresponding confidencethresholds; and identify the pattern from the plurality of patternsstored in the memory device based, at least in part, on comparing theconfidence level with the confidence threshold, wherein the object isprocessed according to the identified pattern.
 9. The system of claim 8,wherein the processing device is further configured to: compare theimage data with the plurality of patterns; and determine a plurality ofconfidence levels based on comparing the image data with the pluralityof patterns, wherein the identified pattern corresponds to a highestconfidence level associated with the plurality of confidence levels. 10.The system of claim 8, wherein the processing device is furtherconfigured to: compare the image data with the plurality of patterns;determine a plurality of confidence levels based on comparing the imagedata with the plurality of patterns; and compare the plurality ofconfidence levels with the corresponding confidence thresholds, whereinthe identified pattern is associated with the corresponding confidencethreshold that is first met by comparing the plurality of confidencelevels.
 11. The system of claim 8, wherein the plurality of patterns arepriority weighted, wherein the pattern is identified by comparing theimage data with the plurality of patterns and identifying a number ofpatterns associated with the corresponding confidence thresholds thatare met by comparing the image data, and wherein the identified patternhas a highest priority weighting as among the plurality of patterns. 12.The system of claim 8, wherein the object comprises a manufactured part,and wherein the image data is associated with an image of a label or aserial number located on the portion of the object.
 13. The system ofclaim 12, wherein the object is processed as part of an operationassociated with monitoring an inventory of manufactured parts.
 14. Thesystem of claim 12, wherein the object is processed by identifying ausage of the manufactured part.
 15. A non-transitory memory devicehaving stored thereon computer-executable instructions that, in responseto execution by a processing device, cause the processing device toperform operations comprising: obtaining image data of an object,comparing the image data with a stored pattern, wherein the storedpattern identifies spatial information of corresponding patternelements; determining a confidence level of the comparison of the imagedata according to a success in matching the image data with the patternelements; comparing the confidence level with a confidence thresholdassociated with the pattern, wherein a plurality of stored patterns areassociated with corresponding confidence thresholds; and identifying thestored pattern from the plurality of stored patterns based, at least inpart, on comparing the confidence level with the confidence threshold,wherein the object is processed according to the identified storedpattern.
 16. The memory device of claim 15, wherein the operationsfurther comprise: comparing the image data with the plurality of storedpatterns; and determining a plurality of confidence levels based oncomparing the image data with the plurality of stored patterns, whereinidentifying the stored pattern comprises identifying the stored patterncorresponding to a highest confidence level from among the plurality ofconfidence levels.
 17. The memory device of claim 15, wherein theoperations further comprise: comparing the image data with the pluralityof stored patterns; determining a plurality of confidence levels basedon comparing the image data with the plurality of stored patterns; andcomparing the plurality of confidence levels with the correspondingconfidence thresholds, wherein identifying the stored pattern comprisesidentifying the stored pattern associated with the correspondingconfidence threshold that is first met by comparing the plurality ofconfidence levels.
 18. The memory device of claim 15, wherein the objectcomprises a manufactured part, and wherein the image data is associatedwith an image of a label or a serial number located on the manufacturedpart.
 19. The memory device of claim 18, wherein the object is processedas part of an operation associated with monitoring an inventory ofmanufactured parts.
 20. The memory device of claim 18, wherein theobject is processed to identify a usage of the manufactured part.